This index is an ongoing study, updated and refined quarterly. Many of the cities studied are now approaching their data collection limits, which brings us to the next phase of development: identifying and collecting data that measures bio-capacity and human anthropogenic footprints in greater depth. This is an iterative process that requires collaboration with all levels of government, academia and industry. The goal is to create tools that give people greater visibility into the human impact on the biosphere and identify areas where solutions can have a substantial impact.
All formulas and equations used are published at the bottom of this article.
121 Québec City
142 Richmond Hill
125 Greater Sudbury
140 St. Johns
125 Saint John
107 Thunder Bay
107 St. Catharines
One indicator was revised.
- Housing Demographics Weighting was added for all building classifications
In this edition the updates listed above were included. Where possible, links to the data updates were added to the downloadable spreadsheets.
- 24 indicator datasets plus 2 calculated indicators were published
- 1300 data points were scored
- 93.8% of all data was collected
- 183 data points were updated since the last release
- 897 source data web links were published
- 50 detailed spreadsheets with score details and data sources, one for each city
- 81 data points were floor scored and 12 were ceiling scored
- Normalized Values are comparative values between zero and one which are ideal for indicator data points. Cities are split into "City Size Categories" then maximum and minimum's are selected from cities within those categories. This allows for the comparison of cities in a meaningful way.
- Floor Scores use the minimum value for indicator data points within a City Size Category. When data is missing for an indicator it is assigned zero, but if that indicator is needed in the calculation of another indicator the floor score is used; Our tests show this method is fair and more accurate.
- Ceiling Scores are used when a city data point is extremely large and if used it would dwarf all other city scores. The next highest score is used to calculate the maximum, then, the city is awarded the maximum score within its City Size Category. Ceilings are caused by unusual circumstances that go beyond the current scoring method and warrant further study.
- Missing data points are replaced with a provincial default where possible. If no provincial statistic exists then the data point is zeroed.
- Indicator weighting is now used for temperature extremes. In future editions weighting will be applied to other indicators so as to reflect their anthropogenic impact more accurately.
- Data Freshness does not effect scores. It is a data column in city detail spreadsheets that reports average date of data collected for each indicator. It is the year average of the top 50% of newest dates for each indicator. The average date is YYYY.N where N is rounded to one decimal point.
Our research shows that smaller cities have smaller eco-footprints which is an inescapable fact due to scale! Larger cities have mass transit infrastructure which is not viable in smaller cities, so classifying cities by size makes comparative analysis possible and fair to all cities studied.
Smaller populations have smaller footprints. They require less space for residential, commercial, industrial, recreation and other human activities; all of which make footprints smaller. Lower is better.
Measured in square km. Larger municipal boundaries create larger city footprints. Annexation and amalgamation are how cities grow in size and this is always done for economic reasons. The exceptions to this rule are Halifax and Saguenay; both of these cities have extensive wilderness reserves inside their boundaries which skews both Municipal Area and Population Density indicators. For this reason they are given ceiling scores for both indicators. Smaller is better.
Population Density: calculated from multiple data points
Protected greenspace areas are subtracted from Municipal Area because it reflects a more realistic density calculation. Higher population densities mean less space is used for more human activity. It is a measurement of footprint efficiency thus making higher density scores more desirable. It is also an indirect measurement of urban sprawl which has become the largest cause of greenspace loss inside city limits. Wilderness Reserve Area's are also included in this calculation. Higher is better.
Population Growth Pressure:
The percentage of population growth. Slower population growth requires less space to grow into which means smaller footprint growth. City's with negative growth are assigned zero growth because negative growth is currently beyond the scope of this study. Lower is better.
Travel To Work By:
This indicator measures a population's uptake of public transit, cycling and walking commuting habits. It is a percentile measurement where higher scores are better. In this edition no footprint weighting factors were used.
- Personal Automobile weighting factor is ZERO (excluded currently)
- Public Transit weighting factor is 1 (Buses, Trains, Infrastructure Footprints)
- Cycling weighting factor is 1 (Bike Paths, Parking, Infrastructure Footprints)
- Walking weighting factor is 1 (No infrastructure required)
Driving Distance For Solo Commutes:
This indicator scores the median driving distance in kilometers for solo automobile commuters travelling to work. Lower is better.
Workforce Commuting Outside City:
Measures the percentage of the workforce traveling outside the city for employment. This causes heavy traffic and poor air quality. Lower is better.
Economic Activity: Under Review
Measures GDP and GDP Growth. Currently higher is better but this indicator is very subjective and will likely be removed in a future edition. Higher is better.
This indicator scores residential housing footprints. Until cities start publishing their housing statistics we are stuck using Municipal Census data as a proxy. We use a weighting factor to approximate the land use areas. There is a tab in each cities detail spreadsheet listing building type weights. Smaller is better.
Air Pollution Emissions:
It is a measure of Total Particulate Matter (TPM) The substances measured are: Particulate Matter 10 Microns or less, Particulate Matter 2.5 Microns or less, Sulfur Oxides, Nitrogen Oxides, Volatile Organic Compounds and Carbon Monoxide. Lower is better.
Solid Waste Tonnage:
Total tonnage of garbage before recycling redirect percentage. Lower is better
Recycling Diversion Rate:
Percentage of waste recycled and redirected away from landfills. Higher is better
Organic Waste Tonnage:
Total organic matter tonnage collected by city from all sources. Higher is better
Domestic Water Usage: Lower is better.
Provinces track Greenhouse Gas (GHG) emissions and publish data for the whole province. Cities are now expected to report GHG emissions to provincial and/or federal government agencies and we will use that data as it becomes available. Lower is better.
Renewable Electrical Capacity:
This is a percentage calculation. Some cities measure their own capacity but most rely on Provincial statistics. Higher is better.
Green Initiatives On Website:
We do not score content and only the existence of an ecological initiatives landing page with links to recycling, green space initiatives, protection programs, etc.. We maintain links to it for all cities in the index. A clear landing page scores higher.
Temperature Extremes Summer And Winter: Merged
This indicator uses two data point measurements. which when combined measure the change in hot and cold extremes as climates changes in studied cities. Lessened cold extremes can allow some invasive species to gain a foothold. Extreme hot weather places stress on much of the urban fauna and wildlife and it also causes peak demand on utilities for air conditioning. Cold temperatures are weighted at 40% while hot temperatures are weighted at 60%. Milder is better.
The Actuaries Climate Index™ is an objective measure of observed changes in extreme weather and sea level changes in coastal cities. It is intended to provide a useful monitoring tool of climate trends and will be updated quarterly as data for each meteorological season becomes available. Lower is better.
Parkland connects us to nature and helps build ecological empathy. Parks are also habitats for nature, so the more park space a city has, the more eco-friendly it is. Higher area is better.
More parks mean greater residential accessibility. The closer the proximity to parks, the more likely we are to walk in them and experience nature. Also, they can become wildlife corridors for insects, birds, etc..
Higher is better.
This is what nature is all about! Ecological empathy is cultivated when we regularly visit nature reserves and experience the wildlife and fauna. Higher is better.
Biological Temperate Zone:
It uses the Plant Hardiness Index of each city. The higher the number the more biologically friendly the year round climate is. Higher is better.
Data Completeness: calculated from all city data points
The total number of indicators data points collected including unreleased indicator data points. When cities measure and track things, they become visible and gain value which can be included in planning decisions.
Higher is better.
It is the sum of all indicators times 10 then rounded to the nearest whole number. This removes the need for a decimal places which makes the score more readable for public audiences.
Formulas are further refined in city spreadsheet details
Click scoreboard then click a city name then click download spreadsheet
City Size Categories
Population Growth Pressure
Travel to Work by
Driving Distance For Solo Commutes
Workforce Commuting Outside City
(Under Review) Economic Activity
Air Pollution Emissions
Organic Waste Tonnage
Domestic Water Usage
Renewable Electrical Capacity
Green Initiatives On Website
(Merged) Temperature Extremes Winter
(Merged) Temperature Extremes Summer
Biological Temperate Zone
Determines grouping for normalization max / min calculations
Score = 1 - Normalized Population
Score = 1 - Normalized Area
Score = Normalized(Pop. / (Municipal Area - Greenspace Area))
Score = Normalized percentage growth, if < 0 use 0
Score = Sum of Normalized(Percentages x Weighting Factor)
Score = 1 - Normalized Median Driving Distance
Score = 1 - Normalized(Population x Percentage)
Score = Sum of Normalized(GDP,GDP Growth, etc..)
Score = 1 - Normalized sum of (Structure Percentile x Weighting)
Score = 1 - Normalized(sum of Air Pollutants)
Score = 1 - Normalized Garbage Tonnage
Score = Normalized Percentage of Garbage Recycled
Score = Normalized Curbside Composte Tonnage
Score = 1 - Normalized Water Usage
Score = 1 - Normalized City GHG
Score = Renewable Capacity / Total Capacity
Score = 1 for a clear landing page, Otherwise score = 0
Score = | Normalized (Winter Min Monthly Avg) | x 40%
Score = 1 - | Normalized (Summer Max Monthly Avg) | x 60%
Score = 1 - normalized (Actuaries Climate Index)
Score = Normalized (Sum of Parkland Area)
Score = Normalized (Park Count)
Score = Normalized (Sum of Wilderness Area)
Score = (Plant Hardiness Index) / 100
Score = (Indicator Count - Missing Data Points) / 10
Score = (Sum of Indicator Scores) x 10